Asymmetrical fuzzy logic control-based MPPT algorithm for stand-alone photovoltaic systems under partially shaded conditions

Document Type : Article


Department of Electrical Engineering, Delhi Technological University, Bawana Road, Delhi, India-110042


Partial shading conditions (PSCs) in Photovoltaic (PV) system is an inevasible situation which curtails the PV array output by exhibiting multiple peaks in its Power-Voltage (P-V) curve. The multiple peaks consist of a single global maximum power point (GMPP) and many local maximum power points (LMPP). The presence of multiple peaks makes tracking of maximum power point more difficult and demands an efficient controller to track the global peak of the P-V curve. In the present work, a novel intelligent asymmetrical Fuzzy Logic Control (AFLC) based maximum power point tracking (MPPT) algorithm has been proposed for tracking GMPP. The fuzzy membership functions of the proposed algorithm have been optimized using a heuristic approach. The algorithm has been designed, developed and analyzed using MATLAB/Simulink. Furthermore, to establish the superiority of proposed AFLC algorithm, it has been compared with conventional perturb & observe (P&O) algorithm and intelligent Fuzzy Logic Control (FLC) based algorithm for GMPP tracking and shading losses under standard test condition (STC) and partially shaded conditions.


1. Ellabban, O., Abu-Rub, H., and Blaabjerg, F. Renewable
energy resources: current status, future
prospects and their enabling technology", Renew. Sustain.
Energy Rev., 39(C), pp. 748{764 (2014).
2. Abdourraziq, M.A., Maarou , M., and Ouassaid, M.
A new variable step size INC MPPT method for PV
systems", Int. Conf. Multimed. Comput. Syst. Proc.,
55(7), pp. 1563{1568 (2014).
3. Koutroulis, E. and Blaabjerg, F. A new technique for
tracking the global maximum power point of PV arrays
operating under partial-shading conditions", IEEE J.
Photovoltaics, 2(2), pp. 184{190 (2012).
4. Patel, H. and Agarwal, V. Maximum power point
tracking scheme for PV systems operating under partially
shaded conditions", IEEE Trans. Ind. Electron,
55(4), pp. 1689{1698 (2008).
5. Ji, Y.H., Jung, D.Y., Kim, J.G., et al. A real maximum
power point tracking method for mismatching
compensation in PV array under partially shaded
conditions", IEEE Trans. Power Electron., 26(4), pp.
1001{1009 (2011).
6. Al-Majidi, S.D., Abbod, M.F., and Al-Raweshidy, H.S.
A novel maximum power point tracking technique
based on fuzzy logic for photovoltaic systems", Int. J.
of hydrogen Energy, 43(31), pp. 14158{14171 (2018).
7. Liu, C., Wu, B., and Cheung, R. Advanced algorithm
for MPPT control of photovoltaic systems", In: Proceedings
of the Canadian Solar Buildings Conference,
Montreal; August, pp. 20{24 (2004).
8. Azab, M.A. New maximum power point tracking for
photovoltaic systems", International Journal Electrical
Electron. Engineering, 2(8), pp. 1600{1603 (2009).
9. Chaouachi, A., Kamel, R.M., and Nagasaka, K.A.
Novel multi-model neuro-fuzzy-based MPPT for
novel multi-model neuro-fuzzy-based MPPT for threephase
grid-connected photovoltaic system photovoltaic
system", Sol. Energy, 84(12), pp. 2219{2229 (2010).
10. Reisi, A.R., Moradi, M.H., and Jamasb, S. Classi cation
and comparison of maximum power point tracking
techniques for photovoltaic system", Renew. Sustain.
Energy Rev., 19, pp. 433{443 (2013).
11. Tsai, H.F. and Tsai, H.L. Implementation and veri
cation of integrated thermal and electrical models
for commercial PV modules", Sol. Energy, 86(1), pp.
654{665 (2012).
12. Ali, O.A.M., Ali, A.Y., and Sumait, B.S. Comparison
between the e ects of di erent types of membership
functions on fuzzy logic controller performance", Int.
Journal of Emerging Engg. Research and Tech., 3(3),
pp. 76{83 (2015).
13. Asim, N., Sopian, K., Ahmadi, S., et al. A review on
the role of materials science in solar cells", Renewable
Sustainable Energy Rev., 16(8), pp. 5834{5847 (2012).
14. Esram, T., Kimball, J.W., Krein, P.T., et al. Dynamic
maximum power point tracking of photovoltaic arrays
using ripple correlation control", IEEE Trans. Power
Electron., 21(5), pp. 1281{1291 (2006).
15. Balasankar, R., Arasu, G.T., and Christy Mano Raj,
J.S. A global MPPT technique invoking partitioned
estimation and strategic deployment of P&O to tackle
partial shading conditions" , Sol. Energy, 143, pp. 73{
85 (2017).
16. Kharb, R.K., Shimi, S.L., Chatterji, S., et al. Modeling
of solar PV module and maximum power point
tracking using ANFIS", Renew. Sustain. Energy Rev.,
33, pp. 602{612 (2014).
17. Sahoo, S.K. Solar photovoltaic energy progress in
India: a review", Renew. Sustain. Energy Rev., 59,
pp. 927{939 (2016).
18. Ishaque, K., Salam, Z., and Syafaruddin A comprehensive
MATLAB simulink PV system simulator with
3174 P. Verma et al./Scientia Iranica, Transactions D: Computer Science & ... 27 (2020) 3162{3174
partial, shading capability based on two-diode model",
Sol. Energy, 85(9), pp. 2217{2227 (2011).
19. Karami, N., Moubayed, N., and Outbi, R. General review
and classi cation of di erent MPPT techniques",
Renew. Sustain. Energy Rev., 68(1), pp. 1{18 (2017).
20. Li, G., Jin, Y., Akram, M.W., et al. Application
of bio-insopired algorithms in maximum power point
tracking for PV system under partial shading conditions",
A review. Renewable and Sustainable Energy,
81(1), pp. 840{873 (2018).
21. Singh, N. A modi ed variant of grey wolf optimizer",
Int. Journal of Science & Tech., 27(3), pp. 1450{1466
(2018). DOI: 10.24200/SCI.2018.50122.1523
22. Vaez, S.R.H. and Minaei, Z. Pulse extraction
of pulse like ground motions based on particle
swarm optimization algorithm", Int. Journal of Science
& Tech., 27(1), pp. 134{158 (2018). DOI:
23. Ram, J.P., Babu, T.S., and Rajasekar, N. A comprehensive
review on solar PV maximum power point
tracking techniques", Renew. Sustain. Energy Rev.,
67, pp. 826{847 (2017).
24. Kumar, P. and Mahajan, A. Soft computing techniques
for the control of an active power lter", IEEE
Trans. Power Deliv., 24(1), pp. 452{461 (2009).
25. Sundareswarm, K., Sankar, P., Nayak, P.S.R., et
al. Enhanced energy output from a PV system under
partial shaded conditions through arti cial bee
colony", IEEE Trans. Sustain. Energy, 6(1), pp. 198{
209 (2015).
26. Verma, P., Mahajan, P., and Garg, R. Comparison
of intelligent and conventional MPPT algorithms for
photoVoltaic system under partially shaded conditions",
IEEE International Conference, RDCAPE-
2017, India, pp. 505{510 (2017).
27. Gow, J.A. Development of a model for photovoltaic
arrays suitable for use in simulation studies of solar
energy conversion systems", 6th Int. Conf. Power
Electron. Variable Speed Drives, Nottingham, UK, pp.
69{74 (1996).
28. Al-Gizi, A., Al-Chlaihawi, S., Louzazni, M., et al. Genetically
optimization of an asymmetrical fuzzy logic
based photovoltaic maximum power point tracking
controller", Advances in Elec. and Computer Engg.,
17(4), pp. 69{76 (2017).
29. Liu, C.L., Chen, J.H, Liu, Y.H., et al. An asymmetrical
fuzzy-logic-control-based MPPT algorithm for
photovoltaic systems", Energies, 7, pp. 2177{2193
30. Kuo, T.J. and Chen, J.F. Novel maximum-powerpoint
tracking controller for photovoltaic energy conversion
system", IEEE Trans. Ind. Electron., 48(3),
pp. 594{601 (2001).
31. Casadei, D. Single-phase single-stage photovoltaic
generation system based on a ripple correlation control
maximum power point tracking", IEEE Trans. Energy
Convers., 21(2), pp. 562|568 (2006).
32. Gupta, N. and Garg, R. Tuning of asymmetrical
fuzzy logic control algorithm for SPV system connected
to grid", International Journal of Hydrogen Energy,
42(26), pp. 16375{16385 (2017).
33. El-Dein, M.S., Kazerani, M., and Salama, M.M.A.
Optimal photovoltaic array recon guration to reduce
partial shading losses", IEEE Trans. Sustain. Energy,
4(1), pp. 145{153 (2013).